Tree-informed Rate Optimization Using Potency Enforcement (TROUPE)

This reposiotry is a part of an in-review paper titled Inferring Cell Differentiation Dynamics with Unobserved Progenitors by William Howard-Snyder, Richard Zhang, Henri Schmidt, Michelle Chan, and Ben Raphael. It contains our implementation for Tree-informed Rate Optimization Using Potency Enforcement (TROUPE) as well as code to simulate data and run experiments.

Dependencies

To run our code please clone this repository and cd into it. Install dependencies via

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -e .

Example method usage

To run our method on one trial of the simulated data (with 32 trees and only 5 out of 9 states) run the following command:

bash scripts/troupe_inference_example.sh

This produces a file at troupe/example/results/reg=0/select_potencies/model_dict.pkl for each regularization value in 0, 0.001, 0.003, 0.01, 0.03, ..., 10, 30. Each of these files stores the debiased transition and growth rates at the given regularization value.

To plot the pareto front you can run

python scripts/evaluate_results.py plot-knees \
    -r troupe/example/results \
    -t troupe/example/data/trees.pkl

TROUPE identifies that the number of states in the process is 9 (see the red star in troupe/example/results/figures/loss_vs_num_states.pdf), which corresponds to a regularization strength of 1.

To visualize the infered rates inferred at this pareto front, you can run

python scripts/evaluate_results.py draw-graph \
    -r troupe/example/results/reg=1/select_potencies \
    -p troupe/example/results/reg=1

which saves the differentiation map to troupe/example/results/reg=0.1/select_potencies/figures/inferred_graph.pdf. You can compare these results to the ground truth parameters (see troupe/example/data/ground_truth_diff_map.png) and notice that they are close to the inferred rates.

Experiments

We also provide our code used to run TROUPE and SSE for the exeriments (simulated and TLS data) from our paper. Note that these scripts assume access to a slurm cluster.

Simulated data experiments (Section 4.1)

Scripts for running TROUPE and SSE are located in troupe/scripts/TLS_experiment.

TLS experiments (Section 4.2)

Scripts for running TROUPE and SSE are located in troupe/scripts/sample_efficiency_experiment.